Passive Wireless Sensor for Measuring AC Electric Field in the Vicinity of High-Voltage Apparatus
Bibliographic record
Abstract
A passive wireless sensor for the measurement of ac electric fields emanating from high-voltage (HV) apparatus using radio-frequency (RF) resonant cavities is presented. The sensor is composed of a coaxial transmission line resonator, where the resonant frequency is perturbed by capacitively coupled varactors. HV apparatus induces a bias voltage on the varactors through capacitively coupled electric fields. A printed circuit board on the top of the cavity provides coupling between the cavity and varactors and also between the varactors and the external electric field. The sensor is designed with a resonant frequency in the range of 2.4-2.5 GHz in the industrial, scientific, and medical band. Using capacitive coupling to an HV transmission line with 60 Hz at 1.7 kV, a resonant frequency shift of 7.2 (kHz)/12.5 (V/m) was obtained. The resonance frequency is determined remotely by sending pulses of RF signal to the sensor and recording the ring back of the resonator. RF pulses, with a frequency of 2455 MHz, are used to remotely interrogate the sensor at a repetition rate of 2.5 MHz and a pulsewidth of 100 ns. Downconverting the received signal, a ring back signal is recorded whose analysis determines the resonant frequency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".